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Article: An Ensemble-Based Densely-Connected Deep Learning System for Assessment of Skeletal Maturity

TitleAn Ensemble-Based Densely-Connected Deep Learning System for Assessment of Skeletal Maturity
Authors
KeywordsConvolutional neural network (CNN)
dense connection
ensemble learning
skeletal maturity
Issue Date2022
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6221021
Citation
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, v. 52 n. 1, p. 426-437 How to Cite?
AbstractAssessment of skeletal maturity is important for a clinician to make a decision of the most appropriate treatment on various skeletal disorders. This task is very challenging when using machine learning method due to the limited data and large anatomical variations among different subjects. In this article, we propose an ensemble-based deep learning pipeline to automatically assess the distal radius and ulna (DRU) maturity from left-hand radiographs. At the same time, we adapted the concept of densely connected mechanism in the proposed network architecture to reuse features and prevent gradient disappearance. Therefore, the model acquires two convincing advantages: first, our model preserves the maximum information flow and has a much faster convergence rate. Second, our model avoids overfitting even if training with limited data. The experimental dataset contains 1189 left-hand X-ray scans of children and teenagers. The proposed method achieves 85.27% and 91.68% for radius and ulna classification, respectively. Extensive experiments prove that our model performs better than using other network structures.
Persistent Identifierhttp://hdl.handle.net/10722/283713
ISSN
2020 Impact Factor: 13.451
2020 SCImago Journal Rankings: 2.261
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, SQ-
dc.contributor.authorWang, X-
dc.contributor.authorShen, Y-
dc.contributor.authorHe, B-
dc.contributor.authorZhao, X-
dc.contributor.authorCheung, PWH-
dc.contributor.authorCheung, JPY-
dc.contributor.authorLuk, KDK-
dc.contributor.authorHu, Y-
dc.date.accessioned2020-07-03T08:23:05Z-
dc.date.available2020-07-03T08:23:05Z-
dc.date.issued2022-
dc.identifier.citationIEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, v. 52 n. 1, p. 426-437-
dc.identifier.issn2168-2216-
dc.identifier.urihttp://hdl.handle.net/10722/283713-
dc.description.abstractAssessment of skeletal maturity is important for a clinician to make a decision of the most appropriate treatment on various skeletal disorders. This task is very challenging when using machine learning method due to the limited data and large anatomical variations among different subjects. In this article, we propose an ensemble-based deep learning pipeline to automatically assess the distal radius and ulna (DRU) maturity from left-hand radiographs. At the same time, we adapted the concept of densely connected mechanism in the proposed network architecture to reuse features and prevent gradient disappearance. Therefore, the model acquires two convincing advantages: first, our model preserves the maximum information flow and has a much faster convergence rate. Second, our model avoids overfitting even if training with limited data. The experimental dataset contains 1189 left-hand X-ray scans of children and teenagers. The proposed method achieves 85.27% and 91.68% for radius and ulna classification, respectively. Extensive experiments prove that our model performs better than using other network structures.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6221021-
dc.relation.ispartofIEEE Transactions on Systems, Man, and Cybernetics: Systems-
dc.rightsIEEE Transactions on Systems, Man, and Cybernetics: Systems. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectConvolutional neural network (CNN)-
dc.subjectdense connection-
dc.subjectensemble learning-
dc.subjectskeletal maturity-
dc.titleAn Ensemble-Based Densely-Connected Deep Learning System for Assessment of Skeletal Maturity-
dc.typeArticle-
dc.identifier.emailCheung, PWH: gnuehcp6@hku.hk-
dc.identifier.emailCheung, JPY: cheungjp@hku.hk-
dc.identifier.emailHu, Y: yhud@hku.hk-
dc.identifier.authorityCheung, JPY=rp01685-
dc.identifier.authorityLuk, KDK=rp00333-
dc.identifier.authorityHu, Y=rp00432-
dc.description.naturepostprint-
dc.identifier.doi10.1109/TSMC.2020.2997852-
dc.identifier.scopuseid_2-s2.0-85121845649-
dc.identifier.hkuros310732-
dc.identifier.volume52-
dc.identifier.issue1-
dc.identifier.spage426-
dc.identifier.epage437-
dc.identifier.isiWOS:000731147700045-
dc.publisher.placeUnited States-
dc.identifier.issnl2168-2216-

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